Identification and In-vivo Characterization of a Novel OhrR Transcriptional Regulator in Burkholderia xenovorans LB400
Bibliographic record
Abstract
Transcriptional regulators (TRs) are an important and versatile group of proteins, yet very little progress has been achieved towards the discovery and annotation of their biological functions. We have characterized a previously unknown organic hydroperoxide resistance regulator from Burkholderia xenovoransLB400, Bxe_B2842, which is homologous to E. coli’s OhrR. Bxe_B2842 regulates the expression of an organic hydroperoxide resistance protein (OsmC). We utilized frontal affinity chromatography coupled with mass spectrometry (FAC-MS) and electrophoretic mobility gel shift assays (EMSA) to identify and characterize the possible effectors of the regulation by Bxe_B2842. Without an effector, Bxe_B2842 binds a DNA operator sequence (DOS) upstream of osmC. FAC-MS results suggest that 2-aminophenol binds to the protein and is potentially an effector molecule. EMSA analysis shows that 2-aminophenol attenuates the Bxe_B2842’s affinity for its DOS. EMSA analysis also shows that organic peroxides attenuate Bxe_B2842/DOS affinity, suggesting that binding of the TR to its DOS is regulated by the two-cysteine mechanism, common to TRs in this family. Bxe_B2842 is the first OhrR TR to have both oxidative and effector-binding mechanisms of regulation. This paper reveals further mechanistic diversity TR mediated gene regulation and provides insights into methods for function discovery of TRs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".